Dynamical systems interact with the environment by exchanging information and energy. The system receives inputs from the environment, uses them internally, generates outputs in a causal way, and transmits the outputs to the environment. Rather than simply recognizing images and sounds, the architecture of an intelligent system should permit the system to discover patterns in the inputs (e.g., relationships between objects), predict behaviors, and act on those predictions.
Considerable advancements in the design of serial and parallel computers, robotics, and other artificial intelligence systems have been made. Yet, the ability of these systems to exhibit intelligent behavior is still at a primitive state. Some exemplary architectures include artificial intelligence systems based on expert systems. These represent a large collection of rules used to predict a best guess in new situations. A hierarchical temporal memory (HTM) network computes probability distributions by collecting and updating statistics of input sequences to learn their causes and to create beliefs. These are then passed to higher or lower level nodes. Neural network designs train parameters of a specific network structure using special algorithms that try to achieve a desired input-output behavior typically by minimizing a function of the error between the input and the output.
However, improved intelligent systems are desired to perform tasks such as image recognition, sound recognition, speech recognition, autonomous behavior, predictive behavior in environments that vary widely from training scenarios, and autonomous discovery and understanding of the external environment.